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Keywords: Discovering differentially expressed genes | |||||||||||
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Deliverables: Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit. Journal: Document your progress in your Course Journal. Some tasks may ask you to include specific items in your journal. Don’t overlook these. Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page. |
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Evaluation: NA: This unit is not evaluated for course marks. |
Discovering differentially expressed genes in a yeast cell cycle dataset.
Task…
Let’s look at differential expression of Mbp1 and its target genes using the analysis facilities of the GEO database at the NCBI.
Task…
First, we will search for relevant data sets on GEO, the NCBI’s database for expression data.
“cell cycle”[ti] AND “saccharomyces
cerevisiae”[organism]
.Now proceed to apply what you have learned in the video-tutorial to the yeast cell-cycle study:
Analyze the results.
DSE1
, DSE2
, ERF3
,
HTA2
, HTB2
, and GAS3
. But what
about the MBD complex proteins themselves: Mbp1 and Swi6?The notion of “differential expression” and “cell-cycle dependent expression” do not overlap completely. Significant differential expression is mathematically determined for genes that have low variance within groups and large differences between groups. The algorithm has no concept of any expectation you might have about the shape of the expression profile. All it finds are genes for which differential expression between some groups is statistically supported. The algorithm returns the top 250 of those. Consistency within groups is very important, while we intuitively might be giving more weight to genes that conform to our expectations of a cyclical pattern.
Let’s see if we can group our time points differently to enhance the contrast between expression levels for cyclically expressed genes. Let’s define only two groups: one set before and between the two cycles, one set at the peaks - and we’ll omit some of the intermediate values.
(Mbp1 OR Swi6 OR Swi4 OR Nrm1
OR Cln1 OR Clb6 OR Act1 OR Alg9) AND GSE3635
(Nrm1, Cln1, and
Clb6 are Mbp1 target genes. Act1 and Alg9 are beta-Actin and
mannosyltransferase, these are often considered to be “housekeeping
genes, i.e. genes with unvarying expression levels, especially for qPCR
studies - although Alg9 is also an Mbp1 target. We include them here as
negative controls. CGSE3635 is the ID of the GEO data set we have just
studied). You could have got similar results in the Profile
graph tab of the GEO2R page. What do you find? What does this
tell you? Would this information allow you to define groups that are
even better suited for finding cyclically expressed genes?If in doubt, ask! If anything about this contents is not clear to you, do not proceed but ask for clarification. If you have ideas about how to make this material better, let’s hear them. We are aiming to compile a list of FAQs for all learning units, and your contributions will count towards your participation marks.
Improve this page! If you have questions or comments, please post them on the Quercus Discussion board with a subject line that includes the name of the unit.
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